Image-Text-to-Video
Diffusers
Safetensors
MiniMaxH3ModularPipeline
text-to-video
image-to-video
video-to-video
text-to-audio-video
image-to-audio-video
image-text-to-audio-video
video-to-audio-video
audio-to-audio-video
audio-video-generation
multimodal
synchronized-audio-video
reference-to-audio-video
Instructions to use MiniMaxAI/MiniMax-H3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MiniMaxAI/MiniMax-H3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MiniMaxAI/MiniMax-H3", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
MiniMax H3 Prompt Enhancer, powered by a fine-tuned 350M-parameter model
#58
by geocine - opened
The model is only around 700 MB. You can try it here:
https://huggingface.co/spaces/geocine/MiniMax-H3-Prompt-Enhancer
I’m also working on a complete reference pipeline with WebGPU integration. The goal is to run prompt enhancement privately on WebGPU or CPU, without relying on ZeroGPU or competing with the GPU resources used for local or server-side video generation.